Generating excitement levels for live performances

ABSTRACT

A live performance is monitored by analyzing an input data stream comprising real-time updates related to the live performance. Different sets of excitement levels, excitement curves, alerts and teasers are generated based on the analysis and reported to a plurality of subscribers using any of a variety of mobile communication and/or computing devices.

BACKGROUND

There are many services that provide game highlights, box scores, andperformance commentary for sporting events. However, these servicesgenerally spoil the excitement of any recorded sports performances byrevealing the score, statistics, and highlights of what transpired. Afan seeking to watch highlights of a game that he or she missed or a fanreminded of a game that is being or was missed is typically informed ofthe outcome of the performance, causing the fan to miss out on theexcitement of experiencing the buildup of game's events as they unfold.

Additionally, with the amount of performances available over theInternet and time-shifted to a DVR, sports fans can spend precious hoursviewing a boring game or one that simply turns out as expected.Furthermore, what may be an exciting event for one sports fan may beuneventful to another sports fan. Currently available services merelybroadcast a game's highlights without considering the myriad preferencesof the game's audience that make the game more exciting or less excitingfor individual fans. This results in the inefficient use of the sportsfans' time, a potential decrease in advertisement revenue, and the lossof viewership and does not allow a fan to experience all the thrill ofthe game as it unfolds in front of them.

BRIEF DESCRIPTION OF THE DRAWINGS

The components in the drawings are not necessarily to scale relative toeach other. Like reference numerals designate corresponding partsthroughout the several views.

FIG. 1A is an exemplary user-interface screen that is used to obtain oneor more subscriber preferences from a subscriber.

FIG. 1B is an exemplary user-interface screen that displays differentsets of excitement curves to a subscriber based on the subscriberpreferences obtained in FIG. 1A.

FIG. 2 is a block diagram of one embodiment of hardware components of asystem for generating excitement levels, excitement curves, alerts andteasers for a live performance.

FIG. 2A is a block diagram depicting one embodiment of softwarecomponents of the Application server(s) depicted in FIG. 2.

FIG. 2B is a block diagram depicting one embodiment of the softwarecomponents of the Analytical Server depicted in FIG. 2.

FIG. 3 illustrates one example of a general purpose computing devicewhich can be used to implement any of the computing devices illustratedin FIG. 2.

FIG. 4 is a flow chart describing one embodiment of the general conceptfor generating different sets of excitement levels, excitement curves,alerts and teasers for a live performance.

FIG. 4A is a flow chart describing one embodiment of a process formonitoring a live performance in real-time.

FIG. 4B is an exemplary illustration of the representation of an inputdata stream as a common data structure of play events related to thelive performance.

FIG. 4C is a flow chart describing one embodiment of a process forgenerating different sets of excitement levels for a live performancefor different categories of subscribers.

FIG. 4D is a flow chart describing one embodiment of a process forreporting the different sets of excitement levels for a liveperformance, to a plurality of subscribers.

FIG. 4E is a flow chart describing one embodiment of a process forreporting the different sets of excitement curves to a plurality ofsubscribers.

FIG. 4F is a flowchart describing one embodiment of a process fordetermining a time to start watching a live performance.

FIG. 5 is a flow chart describing one embodiment of a process forgenerating different sets of alerts for a live performance, fordifferent subscriber categories.

FIG. 6 is a flow chart describing one embodiment of a process forautomatically generating different sets of teasers for a liveperformance, for different subscriber categories.

FIG. 7 is a flow chart describing a process for reporting different setsof alerts and teasers to a plurality of subscribers.

DETAILED DESCRIPTION

The technology disclosed herein relates to generating excitementratings. alerts and teasers for live performances. As used herein, alive performance refers to any live action or content that has anelement of uncertainty, suspense, surprise or competitiveness associatedwith it. The disclosed technology directs sports (or other types of)fans and enthusiasts to the best live performances being played, whilepreserving the emotional thrill of watching the live performance playout. Individuals are alerted to the most exciting live performancesbeing played and when to start watching them. Based on this information,individuals can tune into the live performance and jump right into theaction, whether on their television, mobile device, computer, or at thelive venue.

In order to preserve the suspense of events that fans may not be able towatch live, the disclosed technology can indicate only the excitementlevel of a match, without revealing the score or other spoilers, if thefan so desires. Individuals or fans who elect to time shift theirviewing have confidence that the live performance they are watching isgoing to be truly exciting without knowing anything else about the liveperformance, thus allowing them to experience the thrill of the liveperformance as it unfolds in front of them.

In one set of embodiments, the disclosed technology generates differentexcitement levels for live performances based on factors that might makea live performance more exciting or less exciting for differentcategories of subscribers and/or by analyzing live feeds of play-by-playstatistics related to the live performances. In one embodiment, thedifferent categories of subscribers relate to fans of a particular typeof performance, team, league, player, division, conference, game, sport,genre or other variable. In one example, the different categories ofsubscribers include home team fans, visiting team fans and neutral fans.As used herein, a home team fan refers to a subscriber who is a fan of(or otherwise has an affinity for) the team that hosts the liveperformance, the visiting team fan refers to a subscriber who is a fanof (or otherwise has an affinity for) the team opposing the home team,and the neutral fan does not have a preference or affinity for the hometeam or the visiting team. In some embodiments, the live performance mayinvolve more than two teams and/or one or more individuals. In someembodiments, the excitement levels, curves, alerts and teasers describedherein can be generated separately for home team fans, visiting teamfans and neutral fans. When the live performance involves more than twoteams and/or one or more individuals, the excitement levels, curves,alerts and teasers described herein can be generated separately for eachof the multiple teams and/or individuals. Teasers are brief summaries ofone or more compelling aspects of a performance in progress. They may beconstructed from fixed text and/or one or more regular expressions thatmay include (but are not limited to) team names, player names,period/quarter/half, time elapsed, time left, scores, statistics, plays,other game events, player or team performance notes, and gamesituations.

Excitement levels, curves, alerts and teasers can also be generated forother groups of people. For example, excitement levels, curves, alertsand teasers described herein can be generated separately for differentsubscribers based on subscriber's affinity for fast paced games, gameswith huge momentum swings, games with great historical context, or othercategories. Example subscriber categories may relate to the type ofevent that a subscriber finds exciting, such as a crash within an autoracing performance or an occurrence of a fight during a hockey game.

In another set of embodiments, the disclosed technology generates alertsand teasers when the live performance begins to get exciting. Theteasers include sentence fragments that explain why the live performanceis exciting. Different sets of alerts and teasers are generated fordifferent categories of subscribers.

In another set of embodiments, the disclosed technology generatesteasers at all times during a live performance, whether or not theperformance is at a level of excitement to trigger one or more alerts.

The different sets of excitement levels, excitement curves, alerts andteasers are reported to a plurality of subscribers based on one or moresubscriber preferences. In one example, the subscriber preferencesinclude a list of the subscriber's favorite sports, favorite teams,favorite players, types of sport (for example, low scoring games).Subscriber preferences may also include whether the subscriber wishes toreceive alerts and teasers related to the live performance and thesubscriber's desired alert level. The alert level relates to the numberof alerts that the subscriber wishes to receive during a liveperformance and when the subscriber wishes to be notified of suchalerts. Subscriber preferences may also include a subscriber's displaypreference for viewing the different sets of excitement levels,excitement curves, alerts and teasers related to the live performances.Some subscribers may wish to view the different excitement curvesdepending on whether they are a home team fan, a visiting team fan, or aneutral fan. Others may wish to just view a subset of the excitementcurves.

In one embodiment, the different sets of excitement levels are reportedto the plurality of subscribers as different sets of excitement curves.The different sets of excitement curves graphically represent thedifferent sets of excitement levels over different points in time duringthe live performance. In one example, the excitement level for a liveperformance is a rating given to the live performance on a scale of 0 to100, with 0 being a boring event and 100 being an extremely excitingevent. In one set of examples, three (or more) excitement curves aregenerated: one for the home team fan, one for the visiting team fan andone for the neutral fan. In other examples, additional excitement curvesare generated, such as for a fan who does not like one of the teams, afan who likes a team that is a rival to one of the teams or a fan wholikes any close game. Subscribers can view the excitement curves todecide whether to tune in to a live performance or watch a recordedperformance.

In one embodiment, subscribers may view the different sets of excitementlevels, excitement curves, alerts and teasers related to multiple liveperformances, access real-time content related to the live performanceand the like, via an Internet based web site or using an application(collectively referred to herein as a “Sports Excitement RatingApplication 100”) on a computing device (e.g., cellular telephone,laptop computer, desktop computer, tablet computer, smart appliance,etc.) that is referred to herein as the client device. FIG. 1A and FIG.1B are exemplary user-interface screens in the subscriber's clientdevice via which a subscriber can interact with a service that providesthe excitement levels, excitement curves, alerts and teasers describedherein.

FIG. 1A is an exemplary user-interface screen depicted on the clientdevice that is used to obtain one or more subscriber preferences from asubscriber. In one example, one or more subscriber preferences such asthe subscriber's favorite sports, favorite teams, whether the subscriberwishes to receive alerts and teasers related to one or more liveperformances and the subscriber's desired alert level are obtained fromthe subscriber via the Sports Excitement Rating Application 100. Thisinformation can then be stored on the client device and/or transmittedto a server for the service associated with the Sports Excitement RatingApplication 100.

FIG. 1B is an exemplary user-interface screen that displays differentsets of excitement curves to a subscriber based on the subscriberpreferences obtained in FIG. 1A. For example, if the subscriber'sfavorite teams included the home team in a live performance, then a gameexcitement curve related to the home team sentiment is displayed to thesubscriber. In one embodiment, if the subscriber's favorite teamsincluded both the home team and the visiting team participating in thelive performance, then a game excitement curve related to both the hometeam and the visiting team sentiments are displayed to the subscriber.

In the exemplary illustration of FIG. 1B, different excitement curvesfor the home team fans, visiting team fans and neutral fans aredisplayed to a subscriber based on the subscriber's preferences. In theillustrated example, the y-axis of the curve includes qualitativeassignments made to the excitement levels of a live performance. In oneembodiment, a qualitative assignment of a great game is made to a liveperformance whose excitement levels are between 85-100, qualitativeassignment of a good game is made to a live performance whose excitementlevels are between 65-84, a qualitative assignment of an average game ismade to a live performance whose excitement levels are between 40-64 anda qualitative assignment of a dull game is made to a live performancewhose excitement levels are between 0-39. The x-axis of the curveillustrates exemplary points in time in the live performance duringwhich the excitement levels are generated. In the exemplaryillustration, a first excitement curve 102 is a game excitement curvefor the home team fan, a second excitement curve 104 is a gameexcitement curve for the visiting team fan and a third excitement curve106 is a game excitement curve for the neutral fan. The final excitementlevels for the first excitement curve 102, the second excitement curve104 and the third excitement curve 106 are 75, 73 and 71 respectively.While some embodiments will display all three curves, other embodimentsdisplay a subset of the three curves. Additional embodiments can displaymore than three curves or different curves.

FIG. 2 is a block diagram of one embodiment of the hardware componentsof a system for generating excitement levels, excitement curves, alertsand teasers for a live performance. In one embodiment, the system ofFIG. 2 is described with respect to generating excitement levels,excitement curves, alerts and teasers for a live sporting performance;however, the teachings herein apply to non-sport related performances.It is to be appreciated that the technology described herein is notlimited to generating excitement levels, excitement curves, alerts andteasers for a live sports performance. For example, the technologydescribed herein can be utilized to generate excitement levels,excitement curves, alerts and teasers for a reality TV show, a newsevent, a game show, political action, a business show, a drama or otherepisodic content.

In one embodiment, system 200 generates different sets of excitementlevels, excitement curves, alerts and teasers for multiple liveperformances for different subscriber categories by analyzing live feedsof play-by-play statistics/metrics/data related to the live performance.In one set of examples, three sets of excitement levels, excitementcurves, alerts and teasers are generated for the different subscribercategories: one for the home team fan, one for the visiting team fan andone for the neutral fan.

In one embodiment, system 200 includes one or more Web Server(s) 202coupled via a network 204 to one or more Client Devices 206, 208 and210. Network 204 may be a public network, a private network, or acombination of public and private networks such as the Internet. Network204 can be a LAN, WAN, wired, wireless and/or combination of the above.The Client Devices 206, 208 and 210 may include a conventional computingdevice, such as a desktop computer, laptop computer, cellular telephone,a smartphone, tablet computer, TV, set-top box, or any other type ofcomputing system capable of connecting to the Network 204, either by awire or wirelessly. The Client Devices may also include a recordingdevice capable of receiving live performances such as a DVR, PVR orother media recording devices. In one embodiment, the Client Devicescomprise a console 206, a personal computer 208 and a mobile device 210.Although FIG. 2 shows three client devices, it is anticipated that thesystem would be used by many client devices.

Web Server(s) 202 include one or more physical computing devices and/orsoftware that can receive requests from Client Devices 206, 208 and 210and respond to those requests with data, as well as send out unsolicitedalerts, teasers and other messages. Web Server(s) 202 may employ variousstrategies for fault tolerance and scalability such as load balancing,caching and clustering. In one embodiment, Web Server(s) 202 may includeone or more Memcache Server(s) 212 which may serve as a transitory cacheto store client requests and information related to the liveperformance. Memcache Server (212) can be updated/accessed by Web,Server(s) 202, Application Servers (214) and Analytical Server(s) 216.

Web Server(s) 202 maintain, or otherwise designate, one or moreApplication Server(s) 214 to respond to requests received from ClientDevices 206, 208 and 210. Application Server(s) 214 provides access tothe business logic for use by client application programs in ClientDevices 206, 208 and 210. Application Server(s) 214 may be co-located,co-owned, or co-managed with Web Server(s) 202. Application Server(s)214 may also be remote from Web Server(s) 202. In one embodiment,Application Server(s) 214 interact with an Analytical Server 216 and aDatabase Server 218 to perform one or more operations of the disclosedtechnology.

In an exemplary operation of System 200, one or more subscribers onClient Devices 206, 208 and 210 who wish to view different excitementlevels, excitement curves, alerts and teasers related to one or morelive performances invoke the Sports Excitement Rating Application 100via a user interface in the subscriber's client device. In oneembodiment, a list of a subscriber's preferences comprising thesubscriber's favorite sports and teams within each sport that thesubscriber wishes to receive excitement ratings for is received by theSports Excitement Rating Application 100 via a user interface on theclient device (see FIG. 1A).

Analytical Server 216, which may include one or more computing devices,analyzes live feeds of play-by-play statistics related to one or morelive performances from Data Providers 222. Examples of Data Providers222 may include, but are not limited to, providers of real-time sportsinformation such as STATS™, OPTA™ and Sports Direct™. In one embodiment,Analytical Server 216 generates different sets of excitement levels andexcitement curves for one or more live performances for differentcategories of subscribers based on the analysis. In other embodiments,Analytical Server 216 also generates different sets of alerts anddifferent sets of teasers related to the live performances for differentcategories of subscribers. The operations performed by Analytical Server216 are described in more detail below.

Application Server(s) 214 receives the different sets of excitementlevels, excitement curves, alerts and teasers generated by theAnalytical Server 216. Application Server(s) 214 sends the differentsets of excitement levels, excitement curves, alerts and teasers to aplurality of subscribers on Client Devices 206, 208 and 210 via Webserver(s) 202. In one embodiment, Application Server(s) 214 may alsocustomize the different sets of excitement levels, excitement curves,alerts and teasers prior to sending the different sets of excitementlevels, excitement curves, alerts and teasers to the plurality ofsubscribers, based on the subscribers' preferences. The operationsperformed by Application Server(s) 214 are described in more detailbelow. The different sets of excitement levels, excitement curves,alerts and teasers are displayed to the plurality of subscribers via theSports Excitement Rating Application 220 on the subscribers' clientdevices.

Database Server 218 stores the results received from the AnalyticalServer 216 and Application server(s) 214. In one embodiment, the resultsinclude different sets of excitement levels, excitement curves, alertsand teasers related to one or more live performances. Database server218 also stores information such as the real-time updates related to thelive performances received from Data Providers 222, and subscriberpreferences related to a plurality of subscribers in the system.

FIG. 2A is a block diagram depicting one embodiment of the softwarecomponents of the Application server(s) depicted in FIG. 2. In oneembodiment, Application Server(s) 214 includes a Subscriber PreferencesModule 224, and an Excitement Level Results Module 226. SubscriberPreferences module 224 receives a list of subscriber preferences from aplurality of subscribers utilizing the Sports Excitement RatingApplication 220 via Client Devices 206, 208 and 210. In one embodiment,Subscriber Preferences Module 224 includes a list of a subscriber'sfavorite sports, favorite teams, favorite players, whether thesubscriber wishes to receive alerts and teasers related to the liveperformance, the subscriber's desired alert level and the subscriber'sdisplay preference information.

Excitement Level Results Module 226 receives different sets ofexcitement levels and excitement curves related to the live performancesfor different categories of subscribers from the Excitement LevelGeneration Module 230 in the Analytical server 216. Excitement LevelResults module 226 also receives different sets of alerts from theExcitement Alert Generation Module 232 and different sets of teasersfrom the Teaser Generation Module 234 in the Analytical Server 216. Inone embodiment, Excitement Level Results Module 226 reports thedifferent sets of excitement levels, excitement curves, alerts andteasers to a plurality of subscribers of System 200 in accordance withthe subscribers' preferences. FIGS. 4-7 describe the operationsperformed by the software modules in Application Server 214 in moredetail.

FIG. 2B is a block diagram depicting one embodiment of the softwarecomponents of the Analytical Server illustrated in FIG. 2. In oneembodiment, Analytical Server 216 includes a Data Transformation Module228, an Excitement Level Generation Module 230, an Excitement AlertGeneration Module 232, a Teaser Generation Module 234, and a TeaserDictionary 236. Data Transformation Module 228 receives and analyzeslive feeds such as play-by-play statistics related to live performancesfrom one or more Data Providers 222. Excitement Level Generation Module230 generates different excitement levels for different subscribercategories based on the analysis performed by the Data TransformationModule 228.

Excitement Alert Generation Module 232 generates different sets ofalerts for different subscriber categories based on the different setsof excitement levels generated by the Excitement Level Generation Module230. Teaser Generation Module 234 generates different sets of teasersfor different subscriber categories based on the different sets ofalerts generated by the Excitement Alert Generation Module 232. TeaserDictionary 238 includes a plurality of teasers, arranged as differentcategories of teasers. Each category of teasers includes a set of rulesand a set of teasers that define each category. The operations performedby the software modules in the Analytical Server are discussed in detailin FIGS. 4-7.

FIG. 3 illustrates a general purpose computing device which can be usedto implement any of the computing devices shown in FIG. 2. For example,the general purpose computing device described in FIG. 3 can be used toimplement computing devices in the Application server(s) 214, Analyticalserver(s) 216 or any of the client devices 206, 208 and 210. Computingsystem 300 of FIG. 3 includes a System Memory 302, a Processing Unit(also known as a processor) 304 and a System Bus 306 that couplesvarious system components including the System Memory 302 to theProcessing Unit 304. The System Bus 306 may be any of several types ofbus structures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures.

System Memory 302 stores, in part, instructions and data for executionby Processing Unit 304 in order to perform the process described herein.System Memory 302 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) andrandom access memory (RAM). A basic input/output system (BIOS),containing the basic routines that help to transfer information betweenelements within computer 300, such as during start-up, is typicallystored in the ROM. RAM typically contains data and/or program modulesthat are immediately accessible to and/or presently being operated on byProcessing Unit 304.

The system of FIG. 3 further includes a Mass Storage Device 308. MassStorage Device 308, which may be implemented with a magnetic disk drive,an optical disk drive or flash memory, is a non-volatile storage devicefor storing data and instructions for use by Processing Unit 304. In oneembodiment, Mass Storage Device 308 stores the system software forimplementing the processes described herein for purposes of loading toSystem Memory 302.

A user may enter commands and information into the computer 300 throughinput devices such as a Keyboard 312 and Pointing device 314, commonlyreferred to as a mouse, trackball, touch pad or touch screen. Otherinput devices (not shown) may include a microphone, joystick, game pad,satellite dish, scanner, or the like. These and other input devices areoften connected to the Processing Unit 304 through a User InputInterface 310 that is coupled to the System Bus 306, but may beconnected by other interface and bus structures, such as a parallelport, game port or a universal serial bus (USB). A Monitor 316 or othertype of display device is also connected to the System Bus 306 via aninterface, such as a Video Interface 318. In addition to the monitor,computer system 300 may also include other peripheral output devicessuch as Speakers 322 and Printer 324, which may be connected through anOutput Peripheral Interface 320.

Computer system 300 may operate in a networked environment using logicalconnections to one or more Remote Computers 330. Remote Computers 330may be a personal computer, a server, a router, a network PC, a peerdevice or other common network node, and typically includes many or allof the elements described above relative to the computer 300. When usedin a networking environment, the computer 300 is connected to a remotenetwork through a Network Interface or adapter 328.

The components contained in the computer system of FIG. 3 are thosetypically found in computer systems suitable for use with the technologydescribed herein, and are intended to represent a broad category of suchcomputer components that are well known in the art. Thus, the computersystem of FIG. 3 can be a personal computer, mobile computing device,tablet, workstation, server, minicomputer, mainframe computer, or anyother computing device. The computer can also include different busconfigurations, networked platforms, multi-processor platforms, etc.Various operating systems can be used.

The devices of FIGS. 1-3 discussed above can be used to implement asystem that generates different sets of excitement levels, excitementcurves, alerts and teasers for one or more live performances fordifferent categories of subscribers. FIG. 4 is a flow chart describingone embodiment of a process for generating different sets of excitementlevels, excitement curves, alerts and teasers for a live performance. Inone embodiment, the process of FIG. 4 is performed in real time. In oneembodiment, the steps of FIG. 4 may be performed by the software modulesin the Application Server 214 and the Analytical Server 216 described inFIG. 2A and FIG. 2B respectively.

In step 400, a live performance is monitored. The process of monitoringa live performance is discussed in more detail in FIG. 4A. Although theprocess of FIG. 4 is described with respect to monitoring a single liveperformance, it is to be appreciated that the process of FIG. 4 may beperformed for multiple performances (in parallel or serially).

In step 402, different sets of excitement levels are generated for thelive performance for different categories of subscribers. In one set ofexamples, the different categories of subscribers include home teamfans, visiting team fans and neutral fans. The process by whichdifferent sets of excitement levels are generated for the differentcategories of subscribers is discussed in more detail in FIG. 4C.

In step 404, the different sets of excitement levels are reported to aplurality of subscribers. The process by which the different sets ofexcitement levels are reported to a plurality of subscribers isdiscussed in more detail in FIG. 4D and FIG. 4E.

In step 406, it is determined if one or more of the excitement levelsgenerated in step 402 are at or above one or more excitement thresholds.In accordance with the disclosed technology, in order to notifysubscribers when a live performance begins to get sufficiently exciting,it is determined if an excitement level is at or above a certain valuefor a specific amount of time. This value is referred to herein as anexcitement threshold. Excitement thresholds are discussed in more detailwith respect to FIG. 5. If, in step 406, it is determined that none ofthe excitement levels generated in step 402 are at or above one or moreexcitement thresholds and the live performance has not ended, then theprocess continues to monitor the live performance in real-time in step400.

If there are one or more of the excitement levels that are at or aboveone or more excitement thresholds for sufficient amount of time (step406), then in step 408 one or more different sets of alerts aregenerated for the different categories of subscribers. The process ofgenerating different sets of alerts is discussed in more detail withrespect to FIG. 5. In step 410, different sets of teasers are generatedfor the different categories of subscribers. The process of generatingdifferent sets of teasers is discussed in detail in FIG. 6. In step 412,the different sets of alerts and teasers are reported to a plurality ofsubscribers. The process of reporting the different sets of alerts andteasers is discussed in detail in FIG. 7.

In step 414, it is determined if the live performance has ended. If thelive performance has ended, the process ends in step 416. If the liveperformance has not ended, then the process continues to monitor thelive performance in real-time in step 400.

FIG. 4A is a flow chart describing a process for monitoring a liveperformance in real-time. The process of FIG. 4A is one example ofperforming step 400 of FIG. 4. In one embodiment, the process ofmonitoring a live performance is performed by the Data transformationmodule 228 in the Analytical server 216.

In step 418, schedule updates, player rosters and statistics related toan upcoming live performance are received from one or more DataProviders 222. The data can be received electronically via the Internetor by other means. In step 420, an input data stream comprisingreal-time updates related to the live performance is accessed orreceived. In one embodiment, the input data stream comprises files (orother data strictures) of play-by-play statistics related to the liveperformance. In some embodiments, the input data stream is acquired fromData Providers 222. In other embodiments, the input data stream may begenerated manually by subscribers viewing the live performance or by oneor more computers that process the video of the live performance.

In step 422, the input data stream is analyzed. In one embodiment,analyzing the input data stream includes transforming the information inthe input data stream into a common data structure format of play eventsthat describes an individual play or action of the live performance in astructured way. FIG. 4B is an exemplary illustration of therepresentation of an input data stream as a common data structure ofplay events related to the live performance. In one embodiment, the datastructure of play events 428 represents a set of real-time measuredattributes related to the live performance. The set of real-timemeasured attributes may include, but are not limited to a game ID, agame clock time, a game score, a play description, an event type ID, aposition on the field, type of play, players involved, playerstatistics, schedule updates and current team scores. Additional contextdata can also be included The real-time measured attributes may alsoinclude real-time updates related to the live performance received fromsocial networking or other communication services such as Twitter®feeds, Facebook® updates or real-time updates received from one or morethird party information sources.

In step 424, one or more events of interest in the live performance areidentified, based on the analysis. In one example, identifying an eventof interest may include identifying a nail-biter event. As used herein,a nail-biter event refers to a situation or activity during the liveperformance wherein the outcome of the situation or activity is souncertain that it causes the live performance to become more excitingthan usual. For example, in a live sports performance, a nail-biterevent is identified as an event in which the score is close, the leadcan change at any moment and there is only time left for one more scoreor a small number of scores. In another embodiment, a nail-biter eventcan also be identified when or more excitement levels are at or aboveone or more excitement thresholds as discussed in FIG. 5. Other eventsof interest can also be identified, such as baseball game with apotential no-hitter, overtime in soccer, a shutout in American football,etc.

In step 426, the event of interest is reported to a plurality ofsubscribers on their client devices. In one example, the event ofinterest is reported to the plurality of subscribers along with anexcitement level related to the live performance. In one embodiment, theExcitement Level Results Module 226 in the Application Server(s) 214reports the events of interest to the plurality of subscribers on theirclient devices. In some embodiments, the events of interest are reportedby alerts and teaser, rather than separately as described in FIG. 4A.

In one embodiment, the input data stream is analyzed over differenttime-intervals. In one embodiment, a first time-interval is measuredfrom the start of the live performance to a current point in time duringthe live performance. In another embodiment, a second time-interval ismeasured from a point in time after the start of the live performance toa current point in time during the live performance. For example, theinput data stream is analyzed for the most recent X minutes. In thismanner, the input data stream is analyzed over two time-intervals ortime-windows. In other embodiments, more than two time-intervals mayalso be used.

In one embodiment, the analysis of the input data stream over differenttime intervals results in the generation of different types ofexcitement levels related to the live performance. The generation ofexcitement levels is discussed in detail in FIG. 4C. In one embodiment,analyzing the input data stream over the first time-interval (from thestart of the live performance to a current point in time) results in thegeneration of different sets of final excitement levels (referred to asfinal excitement levels) related to the live performance, and analyzingthe input data stream over the second time-interval (from a point intime after the start of the live performance to a current point in time)results in the generation of different sets of instantaneous excitementlevels (referred to as the instant excitement levels) related to thelive performance. The duration of the second time-interval may bepre-determined by the system. The duration of the second time-intervaldetermines how instantaneous the excitement levels related to the liveperformance are. In one example, the duration of the secondtime-interval is one or a few seconds. In one embodiment, the finalexcitement levels are an average of the instant excitement levels.

In one embodiment, the analysis of the input data stream over thedifferent time-intervals is implemented using a time-window based decayalgorithm. The time-window based decay algorithm weights events thatoccur during the live performance based on when the events occur,wherein events of interest that occur in the past have a smaller impacton the events of interest occurring in the present. That is, thetime-window based decay algorithm is basically a system for reducing thesize of the time-interval towards the end of the live performance. Forexample, consider an event of interest such as a score update thatoccurs during the live performance. In one embodiment, each time a scoreupdate is received in real-time, the value of every contributing scoreupdate that occurred from the start of the live performance isre-calculated to determine the different excitement levels for the liveperformance. This results in the generation of final excitement levelsfor the live performance. In another embodiment, each time a scoreupdate is received in real-time, only the new score updates that occurwithin the pre-defined time-interval are considered, the oldscore-updates outside the time-interval are pushed out, and thescore-updates are re-calculated from those that are within thetime-interval to determine the different excitement levels for the liveperformance. This results in the generation of instantaneous excitementlevels for the live performance.

FIG. 4C is a flow chart describing a process for generating differentsets of excitement levels for a live performance for differentcategories of subscribers. The process of FIG. 4C is one example ofperforming step 402 of FIG. 4. In one embodiment, the process of FIG. 4Cis performed by the Excitement Level Generation Module 230 in theAnalytical server 216.

In step 430, the system identifies information (for example, in responseto the analysis of the input data stream performed in step 422 of FIG.4A) related to one or more performance attributes. In one embodiment,the performance attributes represent specific, gradable and qualifiedaspects of one or more high-level characteristics of the liveperformance that quantitatively impact the excitement of the liveperformance. In one embodiment, the performance attributes include avelocity attribute, a parity attribute, a novelty attribute, a momentumattribute and a context attribute related to the live performance.

The velocity attribute measures the energy level of the liveperformance. For example, for a sport such as cricket, the velocity ismeasured by the number of runs scored per ball or over, or the number offours and sixes scored within a period of time. For a sport such asAmerican Football, the velocity may be measured by the yards per play.

The parity attribute measures how close two teams are matched throughoutthe live performance. The parity attribute also compares how one teamscores with respect to the other. For example, the parity attribute maycompare the velocity across two teams. It is to be appreciated that fora live performance to be exciting, the parity should ideally be even andthe velocity should be high.

The novelty attribute measures the uniqueness of individual events thatoccur both before and during a live performance. A triple play inbaseball, a hat trick in soccer or hockey, a comeback, an upset and alast second shot are examples of events that affect the noveltyattribute.

The momentum attribute relates to the shift in the live performancewhich defines which player or team is doing better with respect toanother player or team.

The context attribute defines the existence and extent of any rivalrybetween teams, importance or prominence of the game (for example, aplayoff game vs regular season, or a meeting of two highly ranked teamsas opposed to two obscure ones), the impact of a given event on theoverall playoff picture, or the importance of a particular game within aplayoff series, bracket, or competition. Note that in other embodiments,other attributes can be used instead of or in addition to theabove-described attributes.

In step 432, a score for each of the performance attributes in the inputdata stream is computed. In one example, a velocity score, a parityscore, a novelty score, a momentum score and a context score arecomputed. In one embodiment, step 432 includes computing differentscores (e.g., three different scores) for each of the performanceattributes for the different subscriber categories. For example,velocity attribute scores for the home team fan, the visiting team fanand the neutral fan are computed; parity attribute scores for the hometeam fan, the visiting team fan and the neutral fan are computed;novelty attribute scores for the home team fan, the visiting team fanand the neutral fan are computed; momentum attribute scores for the hometeam fan, the visiting team fan and the neutral fan are computed; andcontext attribute scores for the home team fan, the visiting team fanand the neutral fan are computed. In such an embodiment, fifteenattribute scores are calculated five for the home team fan, five for thevisiting team fan and five for the neutral fan. In other embodiments,more or less than three sets of scores are calculated.

It is to be appreciated that computing different scores for a particularperformance attribute results in the generation of different sets ofexcitement levels for the live performance for the different subscribercategories. In one embodiment, computing different scores for aparticular performance attribute comprises increasing or decreasing thevalue of the attribute based on how the value of the attribute affectsthe excitement of the live performance for the different subscribercategories. For example, in cricket, one way by which the velocityattribute is measured is based on the number of runs scored per over. Inone example, the value of the velocity score may be increased by acertain factor to determine a velocity score for the home team, whilethe value of the velocity score may be decreased by a certain factor todetermine a velocity score for the visiting team.

In step 434, generic attribute weights are applied to each of theperformance attributes for the different subscriber categories. In oneexample, the generic attribute weights are computed based on the timeelapsed in the live performance. For example, the generic attributeweight applied to a velocity attribute at the start of the liveperformance is higher as compared to the generic attribute weightapplied to the parity attribute at the start of the live performance.

In step 436, a mathematical function (e.g., sum, weighted sum, average,weighted average, or other mathematical function) of the scores and thegeneric attribute weights applied to each of the performance attributesis computed to generate the different sets of excitement levels for thelive performance, for the different subscriber categories. In oneembodiment, step 436 includes generating three excitement levels: onefor the home team fan based on the attributes calculated for the hometeam fan, one for the visiting team fan based on the attributescalculated for the visiting team fan, and one for the neutral fan basedon the attributes calculated for the neutral fan.

In one embodiment, step 436 includes generating three instant excitementlevels (home, visiting and neutral) and three final excitement levels((home, visiting and neutral). In one embodiment, only final excitementlevels are generated. In another embodiment, only instant excitementlevels are generated.

In other examples, additional excitement levels may be generated, suchas for a fan who does not like one of the teams, a fan who likes lowscoring games, a fan who likes a team that is a rival to one of theteams, a fan who likes any close game, etc. For example, an excitementlevel for a fan who likes low scoring games, for a game such as cricketmay be determined by increasing or decreasing the velocity score basedon the number of runs scored per over during the live performance.

Additional details relating to computing excitement levels for a liveperformance is set forth in co-pending patent application 13/037,243,filed Feb. 28, 2011, entitled, “Method and System For an OnlinePerformance Service With Recommendation Module” published as US2011/0212756 on Sep. 1, 2011, which is incorporated herein by referencein its entirety.

FIG. 4D is a flow chart describing a process for reporting the differentsets of excitement levels for a live performance, to a plurality ofsubscribers. The process of FIG. 4D is one example of performing step404 of FIG. 4. In one embodiment, the process of FIG. 4D is performed bythe Excitement Level Generation Module 230 in the Analytical Server 216and the Excitement Level Results Module 226 in the Application server(s)214.

In step 438, the different sets of excitement levels for the liveperformance for the different subscriber categories are received fromstep 402 of FIG. 4. In one embodiment, the Excitement Level ResultsModule 226 in the Application Server(s) 214 receives the different setsof excitement levels from the Excitement Level Generation Module 230 inthe Analytical Server 216. In step 440 it is determined if the differentsets of excitement levels correspond to initial excitement levels forthe live performance. If so, then in step 442 it is determined ifdifferent sets of pre-excitement levels for the different categories ofsubscribers have been computed for the live performance. As used herein,a pre-excitement level for a live performance is a predicted excitementlevel for the live performance prior to the commencement of the liveperformance. In one embodiment, the pre-excitement level is computedbased on historical information such as prior performance metricsrelated to the live performance. If there is a pre-excitement level forthe live performance, then in step 444, the initial excitement levelsfor the live performance for the different categories of subscribers areadjusted to incorporate the pre-excitement level. For example, if theinitial excitement level for a live performance is computed as 50 andthe pre-excitement level for the live performance is 25, the initialexcitement level for the live performance may be computed as an averageof the initial and pre-excitement levels.

In step 446, different sets of excitement curves are generated for thedifferent categories of subscribers. In one embodiment, the differentsets of excitement curves are a graph of the different sets ofexcitement levels for the different categories of subscribers atdifferent points in time during the live performance. In one embodiment,the excitement curve is a graph of final excitement levels for a liveperformance from the start of the live performance to a current point intime during the live performance. FIG. 1B is an exemplary illustrationof different sets of excitement curves generated for differentcategories of subscribers.

In step 448, the different sets of excitement curves for the liveperformance are reported to the plurality of subscribers. In oneembodiment, the different sets of excitement curves for the liveperformance are reported to the plurality of subscribers in accordancewith the subscribers' preferences. For example, if the favorite team ofa subset of the plurality of subscribers includes the home team in thelive performance, then the excitement curve for the home team fan isdisplayed to the subset of subscribers. If, for example, the favoriteteams of a subset of the plurality of subscribers include both the hometeam and the visiting team in the live performance, then an aggregateexcitement curve representing the excitement level of both teams isdisplayed to the subset of subscribers.

One embodiment of step 448 includes looking up the subscribers'preference data stored in Database Server 218 and sending the excitementcurve for home team fans to subscribers who have preference dataindicating that the home team is their favorite team, sending theexcitement curve for visiting team fans to subscribers who havepreference data indicating that the visiting team is their favorite teamand sending the excitement curve for neutral fans to subscribers who donot have preference data indicating that the visiting team or the homeis their favorite team. In some embodiment, multiple curves can be sentto and displayed to a subscriber. In some implementations, all curves(or multiple curves) are sent the subscribers and the subscribers'client devices automatically determine which curves to display based onthe subscribers preference data. In another implementation, all curves(or multiple curves) are sent the subscribers and the subscribermanually chooses which curves to display.

In one embodiment, the Excitement Level Results Module 226 in theApplication server(s) 214 directly reports the different sets ofexcitement curves for the live performance to a plurality of subscribersin accordance with the subscribers' preferences by transmitting thedifferent sets of excitement curves electronically to the client devices206, 208 and 210. The excitement curves are displayed to the pluralityof subscribers via the Sports Excitement Rating Application 220 in theclient devices 206, 208 and 210.

FIG. 4E is a flow chart describing a process for reporting the differentsets of excitement curves to a plurality of subscribers. The process ofFIG. 4E is performed at the subscriber's client device in response toreceiving the excitement curves that are generated in step 446 of FIG.4D. In step 450, the different sets of excitement curves for thedifferent categories of subscribers, for the live performance arereceived. In step 452, the subscriber is prompted with one or moredisplay preferences to view the different sets of excitement curves. Instep 454, the subscriber's selection of one or more of the displaypreferences is obtained, via the Sports Excitement Rating Application100 in the subscriber's client device. In one embodiment, the displaypreference of the subscriber may be to view all the different excitementcurves for the live performance, namely, the excitement curve for thehome team fan, visiting team fan and the neutral team fan. In anotherembodiment, the display preference of the subscriber may be to view justa subset of the excitement curves for the live performance. In step 456,the different sets of excitement curves are displayed to thesubscribers, in accordance with the display preference, via the SportsExcitement Rating Application 220 in the subscriber's client device. Asdescribed above, the process of FIG. 4E is for the embodiment wheremultiple curves are sent to the subscriber. A variation on the processof FIG. 4B includes skipping step 452 and automatically choosing whichcurve to display based on the stored subscriber preference (e.g.,favorite team). A different embodiment only sends one (or a subset) ofcurves to the subscriber based on subscriber preference stored at theserver.

In some embodiments, reporting the different sets of excitement curvesto a plurality of subscribers includes providing a watch-from-time forsubscribers to start watching an exciting event related to the liveperformance, for example, when subscribers do not have the time tocommit to watching the entire performance. FIG. 4F is a flowchartdescribing one embodiment of a process for determining a time to startwatching a live performance. In one embodiment, the process fordetermining a time to start watching a live performance is performed bythe Excitement Level Results Module 226 in the Application Server(s)214. In step 458, the different sets of excitement curves for thedifferent categories of subscribers are analyzed to identify peakexcitement levels in the different excitement curves. The different setsof excitement curves may be generated as discussed in step 446 of FIG.4D, in one embodiment. In one example, the peak excitement level refersto the highest excitement level that is obtained in an excitement curve.In step 460 different watch-from-times for the different sets ofexcitement curves of the live performance are determined based on thepeak excitement levels. In one embodiment, the watch-from-time refers toa time that precedes the peak excitement level of the live performanceby a specified amount of time. The specified amount of time is used tobuild up the subscriber's excitement that leads to the peak excitementlevel and may be pre-determined by the system, in one embodiment. Instep 462, the different watch-from-times of the live performance arereported to the plurality of subscribers by the Excitement Level ResultsModule. In one example, the watch-from-time is reported as a specifictime to start watching an exciting event in the live performance. Instep 464, the different watch-from-times of the live performance aredisplayed to the plurality of subscribers via the Sports ExcitementRating Application 220 on the subscriber's client device.

In another embodiment, after computing a watch-from-time based on theappropriate excitement curve and thresholds, the events in the play byplay may be scanned to determine if the watch-from-time should bemodified to include an important event that might otherwise lie outsidethe established excitement thresholds. Examples of this include a goalin soccer that occurs just before the watch-from-time or a punt infootball that begins the start of a drive that later crosses theexcitement thresholds.

In some embodiment of the disclosed technology, alerts are generated tonotify subscribers that a live performance is exciting. Different setsof alerts are generated for different subscriber categories based on thedifferent sets of excitement levels. FIG. 5 is a flow chart describing aprocess for generating different sets of alerts for a live performance,for different subscriber categories. In one embodiment, the process forgenerating different sets of alerts is performed by the Excitement AlertGeneration Module 232 in the Analytical Server 214. The process of FIG.5 is one example of performing step 408 of FIG. 4.

In step 500, the different sets of excitement levels for the liveperformance that are at or above one or more excitement thresholds arereceived (for example, in response to determining if one or moreexcitement levels are at or above one or more excitement thresholds instep 406 of FIG. 4). In one embodiment, the Excitement Alert GenerationModule 232 receives the different sets of excitement levels from theExcitement Level Generation Module 230. In one set of examples, thedifferent sets of excitement levels received from the Excitement LevelGeneration Module 230 correspond to final excitement levels related tothe live performance.

In accordance with the disclosed technology, in order for a subscriberto receive alerts only when a live performance begins to get exciting, asubscriber is not notified that a live performance is exciting as soonas an excitement level is at or crosses an excitement threshold. Rather,an excitement alert is sent to a subscriber when the excitement level isat or above an excitement threshold for a specific amount of time.Accordingly, the disclosed technology minimizes the generation of alertsthat are false positives or false negatives.

In one implementation of the disclosed technology, an alert bucket iscreated for every excitement level received (in step 500) that is at orabove an excitement threshold. In one set of examples, a first alertbucket is created for excitement levels that are at or above a firstexcitement threshold of 75, a second alert bucket is created forexcitement levels that are at or above a second excitement threshold of90 and a third alert bucket is created for excitement levels that are ator above a third excitement threshold of 99. In another set of examples,more than three alert buckets may be created. For example, alert bucketsmay be created for every excitement level that is received whose valueis a multiple of 3 between 0 and 100.

In one embodiment, different alert buckets are created for differentexcitement levels that correspond to the different subscribercategories. In one set of examples, alert buckets for a home team fan, avisiting team fan and a neutral fan are created. It is to be appreciatedthat in some embodiments, the different sets of excitement levels thatare at or above one or more excitement thresholds (received in step 500)may include only an excitement level for the home team fan. In this casean alert bucket is created only for the home team fan. In anotherembodiment, the different sets of excitement levels that are at or aboveone or more excitement thresholds (received in step 500) may includeexcitement levels for the home team fan and the visiting team fan. Inthis case, alert buckets are created for the home team fan and thevisiting team fan.

In one embodiment, the initial size of an alert bucket corresponds tothe amount of time (ie in minutes) required to trigger the one or morealerts for the excitement levels that are at or above one or moreexcitement thresholds. The size of the alert bucket can reduced overtime or reduced near the end of the event.

In step 502 of FIG. 5, the amount of time (in minutes) required totrigger one or more alerts for the excitement levels that are at orabove the one or more excitement thresholds is determined. In oneembodiment, the amount of time (in minutes) required to trigger anexcitement alert is pre-determined by the system and is computed usingequation (1) shown below:

Amount of time (in minutes)=15−0.625*(Excitement threshold *(20−8))−0.15625*(time in minutes elapsed in live performance)  (1)

In one embodiment, the amount of time is reduced by a rate based on afunction of how much the current excitement level is above theexcitement threshold. Accordingly, in step 504, the rate at which toreduce the amount of time required to trigger the different sets ofalerts for the excitement levels is determined. In one embodiment, therate at which to reduce the amount of time required to trigger thedifferent sets of alerts is determined based on a difference between thecurrent excitement level and the excitement threshold and is determinedusing equation (2) as shown below:

Rate=1.0+0.1* max(0, (Current Excitement Level−ExcitementThreshold−5))  (2)

In another embodiment, the amount of time is increased by a rate basedon a function of how much the current excitement level is below theexcitement threshold. Accordingly, in step 504, the rate at which toincrease the amount of time required to trigger the different sets ofalerts for the excitement levels is determined. In one embodiment, therate at which to increase the amount of time required to trigger thedifferent sets of alerts is determined based on a difference between thecurrent excitement level and the excitement threshold and is determinedusing equation (3) as shown below:

Rate=1.0+0.1* max(0, (Excitement Threshold−Current ExcitementLevel−5))  (3)

It is to be appreciated that an excitement alert for a live performancewhich is at a current excitement level of 85 will be triggered fasterthan an excitement alert which is at a current excitement level of 80,given an excitement threshold of 75, based on the rate computed inequation (2). Accordingly, in some embodiments, in step 506, it isdetermined if the different sets of alerts should be triggeredinstantaneously so that the plurality of subscribers can be notified ofthe alerts immediately, based on the rate computed in equation (2). Inone embodiment, an excitement alert will be triggered instantaneously ifa nail-biter event is identified at the one or more of the excitementlevels.

In another embodiment, an excitement alert will be triggeredinstantaneously when an excitement level is above the excitementthreshold and the amount of time remaining in the live performance isless than a threshold value. The threshold value may be pre-determinedby the system. For example, if the threshold value specifies that onlyten minutes are remaining in the live performance and the excitementlevel is at a first excitement threshold of 75, an excitement alert willbe triggered instantaneously to the plurality of subscribers to notifythe subscribers that the live performance is exciting.

Accordingly, in step 506, if a nail-biter event is identified at any ofthe excitement levels and/or if the amount of time remaining in the liveperformance is less than a threshold value, then the amount of timerequired to trigger the different sets of alerts at the different setsof excitement levels is instantaneously reduced to zero minutes in step508. In step 514, the different sets of alerts are reported to theplurality of subscribers. In one example, reporting the alert includesreporting a teaser in the form of a text message such as “ExcitingGame”, along with optionally reporting the current excitement level ofthe live performance. The manner in which the different sets of alertsare reported to a plurality of subscribers is discussed in FIG. 7. Inone example, in step 514, when the subscriber is notified of anail-biter event, the excitement level that is reported to thesubscriber is an average of the instantaneous excitement level and thefinal excitement level.

In step 506, if no nail-biter event is identified at the currentexcitement level and/or if the amount of time remaining in the liveperformance is not less than a threshold value, then it step 510, theamount of time required to trigger the different sets of alerts(obtained in step 502) is reduced by the rate determined in step 504 toobtain the remaining time required to trigger the different sets ofalerts for the different sets of excitement levels. In one example, theremaining time required to trigger an excitement alert for an excitementlevel is computed as the difference between the amount of time (computedin equation (1)) and the rate (computed in equation (2)) as shown inequation (3) below:

Remaining time (in minutes)=Amount of time (in minutes)−Rate (inminutes)  (4)

For example, consider that the current excitement level related to alive performance is 75. For purposes of this example, the currentexcitement level may correspond to an excitement level generated for thehome team fan, the visiting team fan or the neutral fan. In one example,the current excitement level is determined to be at a first excitementthreshold of 75. If the time in minutes elapsed in the live performanceis 30 minutes, the amount of time required to trigger an excitementalert for the current excitement level of 75 is determined as shown inequation (4) below:

Amount of time (in minutes)=15−0.625*(0.75*(20−8))−0.15625*(30)=4.6minutes  (5)

The amount of time (computed in equation (4)) is then reduced by a ratebased on a function of how much the current excitement level is abovethe excitement threshold. In the above example, the rate at which toreduce the amount of time required to trigger an excitement alert at thecurrent excitement level of 75 is computed as shown in equation (5)below.

Rate=1.0+10*max(0,(0.75−0.75−0.05))=1 minute  (6)

Therefore, in the above example, the amount of remaining time requiredto trigger an alert for a current excitement level 75 which is at anexcitement threshold of 75 is reduced to (4.6 minutes −1 minute)=3.6minutes.

Now consider that a new excitement level is generated and the currentexcitement level has changed to 85. The rate at which to reduce theamount of time required to trigger an excitement alert at the currentexcitement level of 85 is computed as shown in equation (6) below.

Rate=1.0+10*max(0,(0.85−0.75−0.05))=1.5 minutes  (7)

Now, based on the new rate determined in equation (6), the amount oftime required to trigger an excitement alert for an excitement level of85 at the excitement threshold of 75 is further reduced to (4.6 minutes−1.5 minutes)=3.1 minutes.

In some embodiments, the amount of remaining time to trigger an alert iscalculated separately for the home team fan, the visiting team fan andthe neutral fan. Therefore, home team fans may receive an alert, whilevisiting team and neutral fans do not (or vice versa or otherpermutation).

In step 512, it is determined if the amount of time required to triggerthe alerts has reached a pre-determined value. In one example, theamount of time reaches a pre-determined value when the differencebetween the amount of time (computed in equation (1)) and the rate(computed in equation (2)) is equal to zero minutes. In the examplediscussed in equations (4), (5) and (6) above, the amount of timerequired to trigger an excitement alert for an excitement level which isat an excitement threshold of 75 is determined to be 3.1 minutes. In oneexample, an excitement alert for the excitement level which is at theexcitement threshold of 75 will be triggered when the difference betweenamount of time and the rate becomes zero minutes.

If the amount of time has reached the pre-determined value, then theprocess of FIG. 5 performs step 412 of FIG. 4 to report the alert forthe live performance in step 514. In one set of examples, different setsof alerts for different subscriber categories (that is, for a home teamfan, a visiting team fan and a neutral fan) are reported to theplurality of subscribers in accordance with the subscriber preferences.The process by which the different sets of alerts are reported to aplurality of subscribers is discussed in FIG. 7. In step 512, if it isdetermined that there is still time remaining to trigger the alerts thenthe process loops back to step 500 to receive the next set of excitementlevels for the live performance.

The process of FIG. 5 discussed above can be used to implement a methodto generate different sets of alerts for a plurality of liveperformances for different categories of subscribers. In anotherembodiment of the disclosed technology, different sets of teasers aregenerated based on the different sets of alerts. In one embodiment, ateaser includes sentence fragments about an excitement alert thatexplains why the live performance is exciting.

FIG. 6 is a flow chart describing a process for automatically generatingdifferent sets of teasers for a live performance, for differentsubscriber categories (ie home team fan, visiting team fan and neutralfan). The process of FIG. 6 is one example of performing step 410 ofFIG. 4. In step 600, different sets of alerts related to the liveperformance are received (for example, from step 408 of FIG. 4). In oneembodiment, the Teaser Generation Module 234 receives different sets ofalerts from the Excitement Level Generation Module 230 in the AnalyticalServer 216. In one embodiment, a single teaser is generated for a liveperformance. In other embodiments, different sets of teasers aregenerated for different subscriber categories (e.g., different teasersfor home team fans, visiting team fans and neural team fans) based onthe different sets of alerts. That is, an alert for a home team fan willhave a teaser selected for the home team fan, an alert for a visitingteam fan will have a different teaser selected for the visiting teamfan, and an alert for a neutral fan will have a different teaserselected for the neutral fan. In other embodiments, the home team fan,the visiting team fan and/or the neutral fan can receive the sameteaser.

In step 602, one or more sets of rules for one or more teaser categoriesare accessed. In one embodiment, the sets of rules are accessed from theTeaser dictionary 238 in the Analytical Server 216. In one set ofexamples, teaser categories for a live sports performance may include,but are not limited to, parity, velocity, defense, comeback andnail-biter. In one embodiment, the rules for each teaser category definewhether a particular excitement alert is relevant to a respective teasercategory. For example, consider that one or more alerts related to ahome team fan, a visiting team fan and a neutral fan are received. Thesets of rules for each teaser category are analyzed to determine if anyof the alerts are relevant to one or more of the teaser categories. Forexample, a set of rules for the “parity” teaser category may include aset of conditions such as “no hitter flag for the pitching team=TRUE,nail-biter flag=TRUE and parity value=0.9 for one or more of the alertsto be relevant to the “parity” teaser category.

In step 604, one or more relevant teaser categories, based on the setsof rules are identified for each of the alerts, for the differentsubscriber categories. For example, a velocity teaser category and adefense teaser category may be identified to be relevant to anexcitement alert for the home team fan whereas a parity teaser may beidentified to be relevant to an excitement alert for a visiting teamfan, and so on.

The different teaser categories each are assigned a priority. In step606, a chosen teaser category of the relevant teaser categories ashaving the highest priority is identified for each of the differentalerts. In one implementation, the teaser categories are organized in ahierarchy, wherein each category is assigned a competitive code thatrepresents a particular aspect of the category that is worth noting. Inone example, a chosen teaser category having the highest priority isidentified as the category with the maximum competitive code.

In step 608, the set of rules for the chosen teaser category areaccessed to identify one or more pre-stored teasers related to thechosen teaser category. In one example, the pre-stored teasers are textand (optionally) a set of variables such as, “No Hitter for <#team_name>in <#inning>” where <#team_name> and <#inning> are variables. In oneembodiment, one or more pre-stored teasers are identified for thedifferent subscriber categories (one or more for home team fans, one ormore for visiting team fans and one or more for neutral fans). If nopre-stored teasers are identified, but the excitement alert indicatesthat the live performance is exciting, a default teaser is selected. Therules can be set up to choose teasers based on the excitement levelperformance attributes (see step 432 of FIG. 4C), information from theinput data stream (see step 420 of FIG. 4A), the play events (see FIG.4B) and/or other data to find relevant teasers.

The various teasers will each be associated with a static or dynamicpriority. A dynamic priority changes based on excitement levelperformance attributes (see step 432 of FIG. 4C), information from theinput data stream (see step 420 of FIG. 4A), and/or the play events (seeFIG. 4B). In step 610, the highest priority teaser of the one or morerelevant teasers identified in step 608 is chosen. In one embodiment, ateaser of a highest priority is identified based on assigning weights toeach of the relevant pre-stored teasers. The weights are assigned basedon a degree to which each of the pre-stored teasers satisfy the rules ofthe chosen teaser category. A teaser of a higher priority is identifiedas the teaser which has the higher weight. In one embodiment, teasers ofhighest priority are separately identified for the different subscribercategories, such as the home team fan, the visiting team fan and theneutral fan.

In step 612, the process performs step 412 of FIG. 4 to report thedifferent sets of teasers to a plurality of subscribers. The process ofreporting the different sets of teasers is discussed in detail in FIG.7.

FIG. 7 is a flow chart describing a process for reporting different setsof alerts and teasers to a plurality of subscribers. The process of FIG.7 is one example of performing step 412 of FIG. 4. In one embodiment,the process of FIG. 7 is performed by the Excitement Level ResultsModule 226 in the Application server 214. In step 700, the differentsets of alerts and teasers for the different categories of subscribersare received (for example, in response to generating the different setsof alerts in step 408, and generating the different sets of teasers instep 410 of FIG. 4). In one embodiment, the Excitement Level ResultsModule 226 in the Application server 214 receives the different sets ofalerts and teasers from the Excitement Alert Generation Module 232 andthe Teaser Generation Module 234 in the Analytical Server 216. In oneset of examples, alerts and teasers for the different subscribercategories such as the home team fan, the visiting team fan and theneutral fan may be received in step 700. In another set of examples,alerts and teasers for only a subset of the different subscribercategories may be received.

In step 702, the different sets of alerts and teasers are reported to aplurality of subscribers in accordance with an alert level specified bythe subscribers. For example, messages are sent via the Internet and oneor more networks (wired and wireless) to the one or more client devices.In one embodiment, subscribers may specify a desired alert level basedon when they wish to be notified of alerts in a live performance. Afirst alert level is specified by subscribers who wish to receive alertswhen the excitement levels of the live performance are at or greaterthan an excitement threshold of 75. Such subscribers are referred toherein as Fanatics. A second alert level is specified by subscribers whowish to receive alerts when the excitement levels of the liveperformance are at or greater than an excitement threshold of 90. Suchsubscribers are referred to herein as Enthusiasts. A third alert levelis specified by subscribers who wish to receive alerts when theexcitement levels of the live performance are at or greater than anexcitement threshold of 99. Such subscribers are referred to herein asCasual Fans. It is to be appreciated that a subscriber who is a Fanaticwill receive more alerts than a subscriber who is an Enthusiast or aCasual Fan. It is also to be appreciated that a subscriber who is aFanatic will receive alerts earlier in an event compared to subscriberswho are Enthusiasts or Casual Fans. The levels for the Fanatic,Enthusiast and Casual Fan can be changed from the levels describedabove.

In step 704, the different sets of alerts and teasers are displayed tothe plurality of subscribers via the Sports Excitement RatingApplication 100 in the subscriber's client device. In one example, anexcitement alert and a teaser for a live performance that is displayedto a subscriber may include a text message such as “Giants at Dodgers,the lead keeps changing”, Excitement Level=79/100 or “An offensiveexplosion in Toronto.” Many different forms of the text for a teaser canbe used. As described, different teasers are sent to differentsubscriber categories based on different excitement levels. For example,home team fans may receive different teasers with different alerts atdifferent times than visiting team and/or neutral fans.

Note that teasers are relevant for other alert types and can betriggered without reaching specific alert levels. In regard to thelatter, teasers can be triggered in conjunction with alerts based onevents or excitement level in a fantasy match-up; individual athleticperformances for favorite athletes, athletes who went to a specificcollege, athletes on a fantasy team, etc.; notifications sent to a fanby friends or sports experts; a notification sent for a favorite team orathlete indicating an upcoming event about to start, a periodic gameupdate, or a scoring event notification; a notification sent out tocommunicate that a specific event or athletic performance may representa potentially valuable wagering opportunity or that one of a person'swagers is currently in a state of great uncertainty and, thus,excitement.

In one embodiment, the step of FIGS. 4, 4A, 4B, 4C, 4D, 4E, 4F, 5, 6 and7 are performed automatically.

In addition to generating excitement levels during live performances,one embodiment includes generating excitement levels for upcomingperformances (predicted excitement) before the performance. Theegenerates excitement levels for upcoming performances by mathematicallyaggregating game context (as defined previously, i.e. team rivalry,playoff race impact, playoff series/game/bracket, etc.), magnitude ofthe number of comments in the real-time conversation surrounding thegiven event leading up to the event (such as in Twitter, Facebook,blogs, sports press, etc.), predicted parity of the upcoming event(e.g., Las Vegas odds/spread), and predicted velocity of the upcomingevent (e.g. Las Vegas over-under for combined score). The predictedexcitement level can fluctuate dynamically as a performance draws closerdue to changing odds, updated playoff picture or variations in chatter.The predicted excitement level for an event is communicated on a 0-100scale. In other embodiments, the predicated excitement level (and/orduring-game excitement level) can be restricted to another range (ie5-95).

One embodiment of the disclosed technology includes a method forgenerating excitement levels for a live performance. The method includesmonitoring a live performance, generating different sets of excitementlevels for the live performance, the different sets of excitement levelsrelate to different sets of subscriber preferences and reporting thedifferent sets of excitement levels to a plurality of subscribers.

One embodiment of the disclosed technology includes a method forgenerating excitement levels for a live performance. The method includesanalyzing an input data stream comprising real-time updates related tothe live performance, identifying one or more events of interest in thelive performance, based on the analyzing, deriving one or moreperformance attributes related to the input data stream and generatingdifferent sets of excitement levels for the live performance based onthe one or more performance attributes for different subscribercategories such that a first set of excitement levels is generated for afirst category of subscribers and a second set of excitement levels isgenerated for a second category of subscribers.

One embodiment of the disclosed technology includes one or moreprocessor readable storage devices having processor readable codeembodied on the processor readable storage devices. The processorreadable code programs one or more processors to perform a methodcomprising analyzing an input data stream comprising real-time updatesrelated to the live performance, identifying one or more events ofinterest in the live performance, based on the analyzing, deriving oneor more performance attributes related to the input data stream,computing different scores for each of the performance attributes in theinput data stream, for different subscriber categories, applying genericweights to each of the performance attributes for the differentsubscriber categories and generating different sets of excitement levelsfor the live performance, for the different subscriber categories basedon a sum of the scores for each of the performance attributes and thegeneric attribute weights applied to each of the performance attributes.

One embodiment of the disclosed technology includes a system forgenerating excitement levels for a plurality of live performances. Thesystem comprises one or more storage devices storing code, acommunication interface and one or more processors in communication withthe one or more storage devices and the communication interface. The oneor more processors are configured to be in communication with aplurality of client devices via the communication interface based on thecode. The code configures the one or more processors to monitor a liveperformance, generate different sets of excitement levels for the liveperformance based on the monitoring and report the different sets ofexcitement levels to a plurality of subscribers. The different sets ofexcitement levels are for different categories of subscribers.

One embodiment of the disclosed technology includes a method forgenerating alerts for a live performance. The method comprisesmonitoring a live performance, generating different sets of excitementlevels for the live performance based on the monitoring, the differentsets of excitement levels relate to different subscriber categories, foreach of the different sets of excitement levels, identifying if any ofthe excitement levels are at or above one or more excitement thresholds,generating different sets of alerts for the different subscribercategories with respect to the excitement levels are at or above one ormore excitement thresholds and reporting excitement alerts that areabove one or more excitement thresholds to a plurality of subscribers.

One embodiment of the disclosed technology includes a method forgenerating alerts for a live performance. The method comprises receivingdifferent sets of excitement levels for a live performance; for each ofthe different sets of excitement levels, identifying if any of theexcitement levels are at or above one or more excitement thresholds;determining an amount of time required to trigger the different sets ofalerts for the excitement levels that are at the one or more excitementthresholds; determining whether the excitement levels have been at theone or more thresholds for the determined amount of time required totrigger, for the excitement levels that are at the one or moreexcitement thresholds; and reporting alerts to a plurality ofsubscribers for those excitement levels that have been determined to beat the one or more thresholds for the determined amount of time requiredto trigger.

One embodiment of the disclosed technology includes one or moreprocessor readable storage devices having processor readable codeembodied on the processor readable storage devices. The processorreadable code programs one or more processors to perform a methodcomprising monitoring a live performance, generating different sets ofexcitement levels for the live performance, based on the monitoring, thedifferent sets of excitement levels relate to different subscribercategories, for each of the different sets of excitement levels,identifying if any of the excitement levels are at or above one or moreexcitement thresholds, determining an amount of time required to triggerthe different sets of alerts for the excitement levels that are at theone or more excitement thresholds and reporting the different sets ofalerts to the plurality of subscribers when the amount of time requiredto trigger the different sets of alerts reaches a pre-determined value.

One embodiment of the disclosed technology includes a system forgenerating alerts for a plurality of live performances. The systemcomprises one or more storage devices storing code, a communicationinterface and one or more processors in communication with the one ormore storage devices and the communication interface. The one or moreprocessors configured to be in communication with a plurality of clientdevices via the communication interface based on the code. The codeconfigures the one or more processors to monitor a live performance,generate different sets of excitement levels for different subscribercategories based on the monitoring, identify if any of the excitementlevels are at or above one or more excitement thresholds, generatedifferent sets of alerts for the different subscriber categories withrespect to the excitement levels are at or above one or more excitementthresholds, and report the different sets of alerts to a plurality ofsubscribers.

One embodiment of the disclosed technology includes a method forgenerating teasers for a live performance. The method comprisesmonitoring a live performance, generating different sets of teasers forthe live performance and reporting the different sets of teasers to thesubscribers. The different sets of teasers includes different teasersfor different categories of subscribers.

One embodiment of the disclosed technology includes a method forgenerating teasers for a live performance. The method comprisesreceiving different sets of alerts related to the live performance,accessing one or more sets of rules for one or more teaser categories,the rules for the one or more teaser categories define whether one ormore of the alerts are relevant to a respective teaser category, for theone or more alerts, identifying one or more relevant teaser categoriesbased on the sets of rules, for the different subscriber categories,identifying a chosen teaser category of the relevant categories ashaving a highest priority, for the one or more alerts, for at least thechosen teaser category, accessing a set of rules for identifying one ormore pre-stored teasers related to the chosen teaser category, for thedifferent subscriber categories, of the one or more pre-stored teasersidentifying a teaser that is of a higher priority, for the differentsubscriber categories and reporting the identified teaser to a pluralityof subscribers.

One embodiment of the disclosed technology includes one or moreprocessor readable storage devices having processor readable codeembodied on the processor readable storage devices. The processorreadable code programs one or more processors to perform a methodcomprising monitoring a live performance, the monitoring comprisingreceiving different sets of alerts related to the live performance,accessing one or more sets of rules for one or more teaser categories,the rules for the one or more teaser categories define whether one ormore of the alerts are relevant to a respective teaser category,automatically generating different sets of teasers for the liveperformance based on the sets of rules, the different sets of teasersrelate to different subscriber categories and reporting the differentsets of teasers to a plurality of subscribers.

One embodiment of the disclosed technology includes a system forgenerating teasers for a plurality of live performances. The systemcomprises one or more storage devices storing code, a communicationinterface, and one or more processors in communication with the one ormore storage devices and the communication interface. The one or moreprocessors are configured to be in communication with a plurality ofclient devices via the communication interface based on the code. Thecode configures the one or more processors to monitor a liveperformance, generate different sets of teasers for the live performanceand report the different sets of teasers to a plurality of subscribers,the different sets of teasers relate to different subscriber categories.

The foregoing detailed description has been presented for purposes ofillustration and description. It is not intended to be exhaustive or tolimit the claims to the precise form disclosed. Many modifications andvariations are possible in light of the above teaching. The describedembodiments were chosen in order to best explain the principles of thetechnology disclosed herein and its practical application, to therebyenable others skilled in the art to best utilize the technology invarious embodiments and with various modifications as are suited to theparticular use contemplated. It is intended that the scope be defined bythe claims appended hereto.

What is claimed is:
 1. A method for generating excitement levels for alive performance, comprising: monitoring a live performance; generatingdifferent sets of excitement levels for the live performance based onthe monitoring, the different sets of excitement levels relate todifferent categories of subscribers; and reporting the different sets ofexcitement levels to a plurality of subscribers.
 2. The method of claim1 further comprising: generating different sets of excitement curves forthe different categories of subscribers from the different sets ofexcitement levels, each excitement curve is a graph of a set ofexcitement levels over time, the reporting the different sets ofexcitement levels comprises reporting the excitement curves.
 3. Themethod of claim 2 wherein generating the different sets of excitementcurves comprises: analyzing an input data stream comprising real-timeupdates related to the live performance; and identifying one or moreevents of interest in the live performance, based on the analyzing. 4.The method of claim 3 wherein generating the different sets ofexcitement curves further comprises: determining information forperformance attributes related to the input data stream based on theanalyzing; computing different scores for each of the performanceattributes in the input data stream for the different subscribercategories; applying generic weights to each of the performanceattributes for the different subscriber categories; and generating thedifferent sets of excitement levels for the live performance for thedifferent subscriber categories based on a sum of the scores for each ofthe performance attributes and the generic attribute weights applied toeach of the performance attributes.
 5. The method of claim 3 wherein:the generating the different sets of excitement curves comprisesanalyzing the input data stream over a first time-interval to generatedifferent sets of final excitement levels related to the liveperformance over time, the first time interval is from a beginning ofthe live performance to a current time.
 6. The method of claim 5wherein: the generating the different sets of excitement curvescomprises analyzing the input data stream over a second time-interval togenerate different sets of instantaneous excitement levels related tothe live performance over time, the second time interval is from a timeafter the beginning of the live performance to a current time.
 7. Themethod of claim 2 wherein: the generating the different sets ofexcitement curves for the different categories of subscribers comprisesgenerating an excitement curve for a home team fan, an excitement curvefor a visiting team fan and an excitement curve for a neutral fan. 8.The method of claim 7 further comprising excitement curve for a hometeam fan is reported to subscribers who have indicated a preference forthe home team; the excitement curve for a visiting team fan is reportedto subscribers who have indicated a preference for the visiting team;and the excitement curve for a neutral fan is reported to subscriberswho have not indicated a preference for the home team or the visitingteam.
 9. The method of claim 1 wherein: generating the different sets ofexcitement levels for the live performance comprises computing differentsets of pre-excitement levels, for the different categories ofsubscribers.
 10. The method of claim 1, wherein identifying one or moreevents of interest comprise identifying a nail-biter event in the liveperformance and reporting the nail-biter event to one or more of thesubscribers.
 11. A method for generating excitement levels for a liveperformance, comprising: analyzing an input data stream comprisingreal-time updates related to the live performance; identifying one ormore events of interest in the live performance based on the analyzing;deriving one or more performance attributes related to the input datastream; and generating different sets of excitement levels for the liveperformance, based on the one or more performance attributes, fordifferent subscriber categories such that a first set of excitementlevels is generated for a first category of subscribers and a second setof excitement levels is generated for a second category of subscribers.12. The method of claim 11 wherein generating the different sets ofexcitement levels for the live performance further comprises: computingdifferent scores for each of the performance attributes in the inputdata stream, for the different subscriber categories; applying genericweights to each of the performance attributes for the differentsubscriber categories; and generating the different sets of excitementlevels for the live performance, for the different subscriber categoriesbased on a sum of the scores for each of the performance attributes andthe generic attribute weights applied to each of the performanceattributes.
 13. The method of claim 11, wherein the one or moreperformance attributes comprise at least one of a velocity attribute, aparity attribute, a novelty attribute, a momentum attribute and acontext attribute related to the live performance.
 14. The method ofclaim 11, further comprising: generating different sets of excitementcurves for the different categories of subscribers from the differentsets of excitement levels, each excitement curve is a graph of a set ofexcitement levels over time.
 15. The method of claim 14, furthercomprising: reporting the different sets of excitement curves for thedifferent categories of subscribers, to a plurality of subscribers inaccordance with one or more subscriber preferences.
 16. The method ofclaim 15, wherein: reporting the different sets of excitement curvescomprises reporting an excitement curve for a home team fan, anexcitement curve for a visiting team fan and an excitement curve for aneutral fan related to the live performance.
 17. The method of claim 15,wherein: reporting the different sets of excitement curves comprisesdisplaying different sets of watch-from-times related to the liveperformance for the different sets of excitement curves, the differentsets of watch-from-times determined based on identifying different setsof peak excitement levels in the different excitement curves for thedifferent subscriber categories.
 18. One or more processor readablestorage devices having processor readable code embodied on the processorreadable storage devices, the processor readable code for programmingone or more processors to perform a method comprising: analyzing aninput data stream comprising real-time updates related to the liveperformance; identifying one or more events of interest in the liveperformance, based on the analyzing; deriving one or more performanceattributes related to the input data stream; computing different scoresfor each of the performance attributes in the input data stream, fordifferent subscriber categories; applying generic weights to each of theperformance attributes for the different subscriber categories; andgenerating different sets of excitement levels for the live performance,for the different subscriber categories based on a sum of the scores foreach of the performance attributes and the generic attribute weightsapplied to each of the performance attributes.
 19. The one or moreprocessor readable storage devices of claim 18 further comprising:generating different sets of excitement curves for the differentcategories of subscribers from the different sets of excitement levels,each excitement curve is a graph of a set of excitement levels overtime; and reporting the different sets of excitement curves for thedifferent categories of subscribers, in accordance with one or moresubscriber preferences.
 20. The one or more processor readable storagedevices of claim 19 wherein generating the different sets of excitementcurves comprises analyzing the input data stream over a firsttime-interval to generate different sets of final excitement levelsrelated to the live performance over time.
 21. A system for generatingexcitement levels for a plurality of live performances comprising: oneor more storage devices storing code; a communication interface; and oneor more processors in communication with the one or more storage devicesand the communication interface, the one or more processors configuredto be in communication with a plurality of client devices via thecommunication interface based on the code, wherein the code configuresthe one or more processors to monitor a live performance, generatedifferent sets of excitement levels for the live performance based onthe monitoring and report the different sets of excitement levels to aplurality of subscribers, the different sets of excitement levels arefor different categories of subscribers.
 22. The system of claim 21wherein: the one or more processors generate different sets ofexcitement curves for the different categories of subscribers from thedifferent sets of excitement levels, each excitement curve is a graph ofa set of excitement levels over time.
 23. The system of claim 21wherein: the one or more processors report the different sets ofexcitement curves to the plurality of subscribers, based on one or moresubscriber preferences, on one or more display devices.